Conventional Android malware detection method can easily be evaded. In this study

we propose a detection method of Android malicious code based on short-term memory network (LSTM)

which makes malware more difficult to evade from detection. In this method

a program analysis framework that combines static and dynamic analysis is proposed at first to get the permission information

protection information and behavior information. Secondly

entrenched features such as ability features and behavior features are extracted from the information that provided by the program analysis framework. With the entrenched features

we design a malware detection method based on LSTM model to distinguish benign applications from the malicious ones. Experimental results demonstrate that our approach is more effective and robust in Android malware detection than the state-of-the-art methods.